End-to-end effective citizen engagement via advanced analytics and sensor-based personal assistant capability (eeceaspa)

ABSTRACT

Providing an end-to-end citizen engagement, in one aspect, may comprise obtaining data of multiple disintegrated sources from one or more of communication and social computing channels via one or more adapters. Data refactoring and management, integration and process orchestration of the data according to a data model as data attributes of the data model may be provided. One or more analytics may be performed based on the data attributes stored according to the data model and input specified to the one or more analytics. One or more results computed by performing the one or more analytics may be provided. One or more application logics supporting one or more front-end applications may be produced. One or more front-end applications for automated sensing of user activities and sensor-based personal assistant capability may be provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No.14/184,237, now U.S. patent Ser. No. 10,176,488 entitled “PERTURBATION,MONITORING, AND ADJUSTMENT OF AN INCENTIVE AMOUNT USING STATISTICALLYVALUABLE INDIVIDUAL INCENTIVE SENSITIVITY FOR IMPROVING SURVEYPARTICIPATION RATE”, filed on Feb. 19, 2014, U.S. patent applicationSer. No. 14/201,081 entitled “ANALYZING A TRUST METRIC OF RESPONSESTHROUGH TRUST ANALYTICS”, filed on Mar. 7, 2014, and U.S. patentapplication Ser. No. 14/136,694 entitled “SURVEY PARTICIPATION RATE WITHAN INCENTIVE MECHANISM”, filed on Dec. 20, 2013, the entire content anddisclosure of which are incorporated by reference herein in theirentirety.

BACKGROUND

The present application relates generally to computers, and computerapplications, and more particularly to citizen engagement and analytics.

There lacks an end-to-end effective citizen engagement platform thatprovides real-time analysis of the effectiveness of a citizenengagement, e.g., the progress of a voting campaign or personal wellnessimprovement, to provide personalized monitoring, tracking, reminding,and alerting users. Most of the campaign monitoring and reporting focuson aggregate progress of the collective users participating in a citizenengagement campaign and may only be specially created for the campaignadministrators.

BRIEF SUMMARY

A method for providing end-to-end citizen engagements, in one aspect,may comprise obtaining data from one or more of communication and socialcomputing channels via one or more adapters. The method may alsocomprise providing data refactoring and management, integration andprocess orchestration of the data according to a data model as dataattributes of the data model, the data obtained from multipledisintegrated data sources. The method may also comprise performing oneor more analytics based on the data attributes stored according to thedata model and input specified to the one or more analytics. The methodmay also comprise providing one or more results computed by performingthe one or more analytics. The method may further comprise producing oneor more application logics supporting one or more front-endapplications. The method may also comprise providing the one or morefront-end applications for automated sensing of user activities andsensor-based personal assistant capability.

The system for providing an end-to-end citizen engagement, in oneaspect, may comprise a data model comprising a set of data types. Anadapter layer may comprise a plurality of adapter executing on aprocessor and obtaining data from one or more of communication andsocial computing channels. A factory layer may comprise a computerexecutable module executing on the processor and adapting, extracting,making inference, reducing and augmenting, and integrating of the dataas data attributes of the data model, the data obtained from multipledisintegrated data sources. An engagement analytics layer may comprise aplurality of analytics application programming interfaces that invokeone or more analytics computations based on the data attributes storedaccording to the data model and input specified to one or more analyticscomputations. An application logic layer may comprise one or moreapplication logics corresponding to one or more front-end userapplications, wherein the one or more results computed by performing theone or more analytics are provided to the user via the application logiclayer and the one or more front-end user applications. Front-endapplications are provided for automated sensing of user micro activitiesand sensor-based personal assistant capability.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates in one embodiment a system design overview forproviding an end-to-end effective citizen engagement.

FIG. 2 illustrates system context in one embodiment of the presentdisclosure.

FIG. 3 is a diagram illustrating system components of End-to-endEffective Citizen Engagement via Advanced Analytics and Sensor-basedPersonal Assistant Capability (EECEASPA) in one embodiment of thepresent disclosure.

FIG. 4 illustrates system architecture in one embodiment of the presentdisclosure.

FIG. 5 is a diagram illustrating an example data flow in one embodimentof the present disclosure.

FIG. 6 is a diagram illustrating a use case for wellness program usingEECEASPA of the present disclosure in one embodiment, e.g., wellnessmonitoring and personalized incentive and tracking.

FIG. 7 shows an example flow that illustrates a use case for wellnessprograms using EECEASPA with HRA in one embodiment of the presentdisclosure.

FIG. 8 shows an example flow that illustrates another use case forwellness programs using EECEASPA in one embodiment of the presentdisclosure.

FIG. 9 and FIG. 10 illustrate a data model in one embodiment of thepresent disclosure.

FIG. 11 shows an example data flow for performing trust and impactanalytics in one embodiment of the present disclosure.

FIG. 12 illustrates software module interaction and APIs in EECEASPAfactory layer in one embodiment of the present disclosure.

FIGS. 13-26 shows example front end user interface screens in oneembodiment of the present disclosure, via which a participant may enterdata, view data, and otherwise interact with a citizen engagementplatform of the present disclosure in one embodiment.

FIG. 27 illustrates software module interaction and APIs in EECEASPAfactory layer in one embodiment of the present disclosure.

FIG. 28 is a diagram showing an example of input and output for ImpactAnalytics in one embodiment of the present disclosure.

FIG. 29 illustrates a schematic of an example computer or processingsystem that may implement a system in one embodiment of the presentdisclosure.

DETAILED DESCRIPTION

Citizen engagement or Campaign refers to a city or citizen initiatedactivity that has goal statement, timeline, and qualification forparticipation.

Campaign Definition may include defining goals, start and end dates,targeted age groups, targeted geographic areas, task for the volunteersto do (e.g., vote for a new park location), incentive definitions, rulesto dynamically adjust incentives, and success metrics or measurementmetrics.

Campaign Announcement or Launch may include generating campaign Web pageand automatically pushing announcement to social media channels.

Campaign Recruitment (online) enables citizens to register ascampaigners and campaigners to update recruitment status; enablecitizens to register as volunteers and perform the task they arerecruited for right there where they are registered; enable businesses(organizations or citizens) to register as sponsors; display campaignrecruitment status for social approval.

Campaign Activity Reporting and Analysis may provide aggregate displayof campaign progress of near real-time activity status for generalpublic consumption and for use by the campaign administrators: e.g.,viewed, liked, followed, response rate, most followed people, temporalstats and advanced analytics for staff consumption, which enables nearreal-time monitoring of the progress of the campaign status andadjustments of incentive based on the response rate and coverage.

Sensor-based refers to using various sensing devices, e.g., Pulse oxymeter, Heart beat monitor, Blood sugar monitor, Pedometer, etc., fordetermining information.

Sensor-based Personal Assistant (PA) Capability refers to using varioussensing devices to create personalized experience and assistance invarious aspects related to a campaign, e.g., sending, monitoring andtracking of personal progress daily toward the personal goals set by theusers, and sending out reminders and alerts based on the goals set outby the users.

Various city/citizen initiated activities may be carried throughcommunication/interaction channels such as community center activities,city infrastructure reporting via phones, public hearing processes, andso forth. Examples of campaigns include trying to persuade a group (alarge number) of people to engage in a goal: e.g., 1) commit to ridetheir bike to work on a given week, 2) install rain gardens in theiryard, 3) replace their incandescent light bulbs with CFLS (compactflorescent light bulbs), 4) help recruit volunteers for a study. Forcity and citizens, the lack of a common single point interactioninterface poses various challenges such as low participation rate, novisibility of campaign activities to city administrators, inefficientcity resource allocation/utilization, and so forth. Campaigns sharecommon characteristics, and the process may be repeatable. They can beclearly defined with goals, a start and stop time, and activities to bedone. Because campaigns happen at particular time (and sometimes place)they can be a social activity with people working together and gettingfriends involved. Because campaigns involve many people doing the samething during the same time, they can use social incentives like leaderboards, badges, rankings and points. Businesses may wish to sponsor themby contributing material incentives like coupons or t-shirts (andparticular campaigns will attract particular sorts of people thatparticular businesses may be interested in). Participants can help oneanother by providing moral support, tips, etc. Campaigns are also wellsuited for test deployments in cities. One city may promote rain gardeninstallation; another might promote bus use. Doing 1 or 2 campaigns isless of a commitment than, say, starting up an online 3ii. 3ii providesfunctions similar to 311, which is New York City's main source ofgovernment information and non-emergency services. There are specificroles in the City (usually the Communications Director) with whom towork.

Wellness specific problems for citizen engagement currently lack ofPersonal Assistant (PA) Capability and a system that communicates withPA. A methodology of the present disclosure may provide such PAcapability in the citizen engagement conducted by the cities to doautomate sensing using wireless sensors/GPS, categorization,self-learning, and automated correction of the categorization ofcommonly performed physical activities, e.g., running, jogging, driving,walking, etc., personalized monitoring and tracking of the sensed data(e.g., via various wireless sensors, e.g., Pulse oxy meter, Heart, beatmonitor, Blood sugar monitor, Pedometer), monitoring and tracking ofdaily personal progress, (e.g., frequency and amount of eating, physicalexercises, and increase/decrease of weight), and sending out remindersand alerts based on the goals created by the citizen to do theactivities to improve the wellness of the citizens.

A methodology of the present disclosure in one embodiment may store,combine, correlate, and analyze public and private data about citizens,city infrastructure, and city and/or citizen initiated engagementsand/or campaigns and their interactions to achieve an effective citizenengagement. The methodology in one embodiment may employ analytics toachieve the effectiveness of a citizen engagement. For example, for agiven activity, an engagement model and analytics may quantify trustlevel of participants, impact assessment, personalized rewardcharacterization, recruitment framework, behavior characterization, andprioritization of activities. In another aspect, an end-to-end EffectiveCitizen Engagement system may be provided that comprises 1) a back-endsystem that processes data from Web sites and social media, mobilephones, and sensing devices and produces analytics to achieve aneffective citizen engagement, 2) End-user interface comprising one ormore smartphone Apps (applications) for automated sensing of personalmicro activities and for logging, viewing, and editing physicalactivities, diet and weight, as well as setting goals and working inteams, and one or more Web applications for users to interact with thesystem.

While the present disclosure describes a methodology with reference to acity and citizens of the city, it should be understood that themethodology of the present disclosure may be applicable to any othergoverning or organizational group and its constituents.

FIG. 1 illustrates in one embodiment a system design overview forproviding an end-to-end effective citizen engagement. A technicalapproach in one embodiment combines public and private data from cityand citizen data sources, use information integration and analytics toprovide value, and deliver value via alerts to city (based on citizendata) and to citizens (based on other citizen data and city data). Suchapproach may also use behavior models as a component of functionalityfor validating citizen input and incentivizing citizen behavior so as toenable understanding of what is happening in the city and provide meansof influencing city level outcomes.

There are various attributes of an individual that may be captured. Eachattribute can play a role in determining an individual's engagementcharacteristics and qualifications. For example, a sustainabilitycampaign may be better for those who are more environmentally aware. Apublic hearing about transit planning would interest more people who usethe public transit system who are being impacted by the transitplanning. Each engagement may have an impact on a diverse set of keyattributes of a citizen, e.g., environmental awareness, physical healthconditions, geospatial existence, social relation, financial conditions,demographic attributes. A deep analytical capability may be provided inone embodiment of the present disclosure that quantifies each attribute.The actual data collected for these set of selected attributes for eachcampaign constitute one of the data sources as shown in FIG. 1 andlabeled as ‘Multi-silo data’ of the EECEASPA focus area.

FIG. 2 illustrates a system context of End-to-end Effective CitizenEngagement via Advanced Analytics and Sensor-based Personal AssistantCapability (EECEASPA) in one embodiment of the present disclosure. Thesystem and methodologies thereof, in one aspect, enable effectiveend-to-end citizen engagements by providing sensing, monitoring,tracking, reminding, alerting, teaming, and personalized assistantcapability. In one embodiment, EECEASPA 202 takes input from variousdata sources, e.g., smartphone Apps (GPS, user inputs) 204, map servers(geo location data) 214, aggregate anonymous participant statistics 218,participants' personal data 222, Survey data / goals recommendationsfrom Health Risk Assessment (HRA) Sites 220, Emails, photos, videos fromcommunication channels 206, and social computing channels (social mediachannels 208, e.g., social networking sites 212, blogging sites,microblogging sites 210). EECEASPA 202 may receive data such as textmessages, sensor data, geographic location data, emails, photos, etc.,from a mobile device such as smartphones 204 via an application(EECEASPA front end application) installed and running on thesmartphones 204. The smartphones 204 (via the application) may alsoreceive data pushed from the EECEASPA 202. EECEASPA 202 may also receivesensor data, e.g., wirelessly from sensor devices 216 that have wirelesscapabilities. Examples of sensor data may include data from pulse oxymeter, heart beat monitor, blood sugar monitor, pedometer, and others.Sensor data may also include data from automated sensing of the commonlyperformed human daily micro activities, e.g., jogging, jumping, running,driving, using sensing devices. Such sensor data may be also receivedfrom smartphones 204 that have such capabilities for sensing.User-assisted learning may be performed based on user-edited entries toself-correct and reclassify system logged activities to improveaccuracy; to automatically detect patterns of misclassification, andautomatically correct misclassification. In one aspect, Sensor-basedPersonal Assistant Capability may be provided that employ sensingdevices and information assistant device for personalized sensing ofeveryday activities, e.g., personalized statistics collecting,monitoring, goal-tracking, displaying physical activities history,editing/adding physical activities, progress-displaying, reminding,alerting, and providing recommendations when interfacing with HRA sites,etc, teaming support to create, update, delete, join, and leave a team,as well as comparing anonymous aggregate data of other members in ateam. Capabilities for monitoring, tracking, goal-setting, teaming, andintegration with the HRA 220, and alerts may be provided based on datacollected to support HRA improvement. Users (participants) 220 and/orcity management/administration 218 may interact with EECEASPA 202 forengagements. EECEASPA may be implemented on one or more computerprocessors, e.g., as software running on the one or more processors.

FIG. 3 is a diagram illustrating system components of End-to-endEffective Citizen Engagement via Advanced Analytics and Sensor-basedPersonal Assistant Capability (EECEASPA) in one embodiment of thepresent disclosure. System architecture 302, for example, shows back-endcomputer or computing components for implementing EECEASPA of thepresent disclosure in one embodiment. A Data model and a database 304may comprise a set of data types and a database that stores andretrieves data as defined by the data model.

An Adapter layer 306 comprises one or more adapters for getting datafrom various communication and social computing channels for the saidarchitecture 302 of citizen engagements. The Adapter layer 306, forexample, may include application programming interfaces (API) or thelike for interface with various data sources from receive and/orexchange data from the various data sources. There may be an API foreach specific data source. Each adapter may process a specific set ofdata, for example, an HRA adapter may process health and/or wellnessrelated data, e.g., weight, diet, and activities/exercise; a transitadapter may process smartphone GPS data of participant's movementrecords in and out of the City, a driver adapter may process drivingdata, e.g., driving habits; a sustainability adapter may processsustainability related data, e.g., water and energy smart meter data.Other adapters may be included, and more examples are described below.

A Factory layer 308 provides data management and integration, andapplication programming interfaces (APIs) for an effective mechanism toconsume data from multiple the disintegrated data sources from variousthe communication and social media channels, transform the data throughadaptation, extraction, inference, reduction, augmentation, andintegration. The Factory layer 308 may retrieve data from the database(DB) 304. The Factory layer 308 supports the architecture 302 forcitizen engagement and process, orchestrate to integrate with the datamodel 304, adapters 306, analytics and application logic 310. Forcross-domain data integration, the factory layer links multipledisintegrated data sources by both dynamic integration at runtime andpre-calculated integration that stores data into database (DB).

An Engagement Analytics layer 312 comprises one or more analytics foranalyzing the data and computing results of the citizen engagements.Examples of analytics are further described below.

An Application Logic layer 310 comprises one or more application logictailored for various application types. An application logic of theApplication Logic layer 310 may provide an application related functionsthat support one specific type of application. For example, thesustainability related application logic for water may provide functionsto support a sustainability application and/or Web portal for water;application logic for electricity may provide functions to support asustainability application and/or Web portal for electricity. Similarly,there may be application logic for health and/or wellness supporting asmart phone Application for health and/or wellness. Each applicationlogic may implement the functions that support one type of application.

A front-end user interface 316 may comprise smartphone apps 314 forautomated sensing of human micro activities and sensor-based PersonalAssistant Capability sense devices to act like a Per Personal Assistantfor personalized sensing. The front-end 316 of the system architecturemay also comprise Web Applications 318, Apps for citizens and City(e.g., shown in FIGS. 2 at 218 and 222) to conduct effective citizenengagements. Web Applications 318 are applications that are Web based oraccessible via Internet.

In one embodiment, EECEASPA takes input from various data sources 320,e.g., smartphone Apps (GPS, user inputs), personalized data, surveys,reports, communication channels (email, etc.), and social computingchannels (social media channels, e.g., social networking sites, bloggingsites, microblogging sites), e.g., as described with reference to FIG.2.

In one embodiment, input data 320 are first processed by the Adapterlayer 306. The Factory layer 308 integrates the data processed by theAdapter layer 306 and transforms the data into the correct format forthe Engagement Analytics Layer 312 to perform calculation and produceresults, which is used by a specific Application logic 310 targeted fora specific type of application to send and/or receive data via APIs tothat target application (e.g., shown at 528 in FIG. 5). Examples of thecalculations performed by the engagement analytics layer may include(but are not limited to): trust analytics that provides a mechanism forsystematic analysis on the quality of data and/or inputs provided bycampaign participants, which can compromise the quality and/oreffectiveness of the campaigns, incentive analytics that provides amechanism to optimize the allocation of incentives resources and providedynamic adjustment of incentive allocation based on a participant'sindividual incentive sensitivity to the changes of incentive amount,e.g., how a participant is responding to the incentive changes, andother analytics. Additional calculations may include impact analyticsthat analyzes the impact on the participants by the campaign, behavioranalytics that analyzes how the participants' behavior affects by thecampaign, reward analytics, recruitment analytics, geo analytics,effectiveness analytics and prioritization analytics, etc.

Table 1 illustrates some of (he analytics by definition and examples ofcorresponding use cases:

Analytics Definition Examples of Use Cases Reputation Assign areputation metric Identify the quality of analytics to citizens for useto reports by participants identify the quality of Detect spammingusers; submission, detect Find domain experts in spamming, find domainsome specific problem experts, prioritize inputs, areas and as input toincentive Help prioritize inputs from analytics, etc. a good qualityreport source Use this metric for the incentive analytics to giverewards for more valuable inputs Impact Based on pre-defined rules, Whena serious pothole analytics link reported incidents and report (e.g.,through 3ii) is comments to impacted submitted, the impact parties,actions, and analytics generates a list of possible outcomes actions tobe taken by different departments, analyzes resources to be assigned,and creates a set of messages to be sent to each department IncentiveGenerate various 3ii: Give higher points to analytics incentives such aspoints, users with fast and badges, ranks, and so forth valuable reportsto have that are linked to reporting, high impact polling, and otheractivities Investment: give higher through a citizen points to userswith engagement platform valuable comments that help shape the publichearing process Recruitment For a given task (e.g., For a electric polerepair framework dispatching a routine patrol request, the analytics andanalytics to a community), select the searches for available right setof people to workers and ranks them in perform the task preferred orderso decision Based on definable rules, makers can choose the identifybest workers for a right one reported incident Social Perform variousanalytics Use to prioritize actions to interaction over the socialprimitives inputs from influential analytics such as identifyingcitizens influential users, Improve the ability to identifying socialgroups detect early indication of based on interaction incident thatneeds dynamics immediate attention Poll analytics Aggregate andsummarize For a simple poll such as inputs from poll ‘communitywalkability’ participants (1 to 5 wherein 1 represents the least and 5represents the most), generate a walkability map showing statisticsSoliciting comments, count a number of positive words and negative wordsand show statistics

Smartphone apps 314 provide various functions, for example, comprising:1) automated sensing, classification, self-correction and learning ofhuman micro mobility activities to produce accurate classification ofthe type and duration of an activity, 2) monitoring, tracking,goal-setting, teaming, and integration with the health risk assessment(HRA) sites, provide alerts based on data collected to support HRAimprovement, etc. In one embodiment of the present disclosure, there maybe more than one smartphone app. For instance, one app may contain twosets of functions. In this example implementation, one set of functionsis implemented by one app. Smartphone apps 314 may be a CEP Appdescribed above.

Both Smartphone Apps (for Automated sensing and Personal Assistantmonitoring) may be downloaded and installed on a participant'ssmartphone with an operating system, e.g., Android™ OS, and/or others.Users take their smartphones with them for physical activities, e.g.,walking or running. Smartphone App for Automated sensing (e.g., at 314)utilizes the sensing device, e.g., Smartphone accelerometer, to provideautomated sensing of the most commonly performed human daily microactivities, e.g., jogging, jumping, running, driving, etc. The Apptransmits the data produced by the use of the sensing device to thebackend server, which performs analytics on the data for certainduration and automatically classifies the data. It can also performuser-assisted learning based on user-edited entries to self-correct andreclassify system logged activities to improve accuracy; automaticdetection of patterns of misclassification; and automatic correction ofmisclassification. Smartphone App for Personal Assistant monitoring,tracking, goal-setting (e.g., at 314) may provide for personalizedstatistics collecting, monitoring, goal-tracking, displaying physicalactivities history, editing/adding physical activities,progress-displaying, reminding, alerting, and providing recommendationswhen interfacing with HRA sites, etc. Smartphone App for PersonalAssistant monitoring, tracking, goal-setting (e.g., at 314) may alsoprovide for teaming support to create, update, delete, join, and leave ateam, as well as compare anonymous aggregate data of other members in ateam. Smartphone App for Personal Assistant monitoring, tracking,goal-setting (e.g., at 314) may also provide for integration with theHRA, and the alerts based on data collected to support HRA improvement.

A user interface 316 (e.g., smartphone apps), Web Applications 318, andApps 314 interact with citizens and City management to enable effectivecitizen engagements between City and its citizens.

FIG. 4 illustrates system architecture in one embodiment of the presentdisclosure, for example, shown at 302 in FIG. 3. The architecture 402for the back-end system in one embodiment comprises a Data model 404, anAdapter layer 406, a Factory layer 408 for Data management/integrationand process orchestration, an Engagement Analytics layer 412, and anApplication logic layer 410. The Data model 404 may comprise a variousdata types such as (but not limited to): Record, Verification, Impact,Location, Actor, RegisteredActor, Comment, Attachment, Account,Preference, Subscription, Notification, Incentive, ActorAction,Template, Relationship, Task, Team, Teammember, and Teamgoal. The Datamodel 404 may include other data types, and a database that stores andretrieves data stored according to the data model.

The Adapter layer 406 may comprise one or more adapters, for example,Social computing or media adapters/APIs, Communication adapters/APIs,and/or Other adapters/APIs, e.g. adapters to interface with a monitoringand tracking platform, e.g., intelligent operations center and adapterfor Health Risk Assessment (HRA) sites, etc. The HRA adapter is used tointerface with a third party system that provides auxiliary information,e.g., a health risk assessment survey.

The Factory layer 408 that provides data management and integration, andprocess orchestration, may comprise the following capabilities:Integration with the Data model 404; integration with the Socialcomputing adapters/APIs; integration with various adapter 406, e.g.,integration with the Communication adapters/APIs, integration with themonitoring and tracking platform for key performance indicators (KPIs)computation and display; backend processing for multiple sensor-basedsmartphone apps.

Generally, the Factory layer 408 may perform following functions:Adaptation; Extracting, inferring, reducing, and augmenting; andintegration.

Adaptation may include importing and/or transforming the source data tothe format required by each target data source consumer, e.g., analyticslayer (e.g., trust, incentive, impact analytics, etc.), applicationlogic, applications, and/or others.

Extracting, inferring, reducing, and augmenting may include extractingspecific data elements required for the output, and inferring based onthe extracted data to produce additional data required by the output(e.g., what was happening at certain time). For example, the locationstamp of a social media photo, textual input of store name may beextracted, then the approximate location, e.g., restaurant, may bedetermined to look up the location identification (e.g., lat/long) andprior location information may be combined. Timestamp of postings andglobal positioning system (GPS) locations may be used to inferinformation. For example, rough location can infer what one might bedoing or interested in, e.g., park or such outdoor activities, homeimprovement, restaurant. Such information may be used in marketing andpromotional offerings applications. As another example, this functionmay integrate multiple and different types of data into an integrateddata item.

Integration may include cross-domain data integration, e.g., a way tolink multiple disintegrated data sources, e.g., sensing data is linkedto multi-domain data, e.g., water, energy, wellness, social networkidentifier, social network email, public wall. For example, a sensingapp may log location information about a user and a social network sitemay have location data from the same user. Combining both data may fillin a gap and provide more complete information.

The integrating and process orchestration of the Factory layer maycomprise integrating with a monitoring and tracking portal, e.g., IBMIntelligent Operations Center for computation and display of theimportant statistics, e.g. key performance indicators (KPIs),integration with the social computing adapters, integration with thecommunication adapters, integration with other adapters, e.g., forHealth Risk Assessment (HRA) sites, which is a third party system thatprovides auxiliary information, e.g., a health risk assessment survey,as well as integration with backend processing for data received frommultiple sensor-based smartphone apps.

The Engagement Analytics layer 412 further comprises analytics andApplication Programming Interfaces (APIs) for Trust analytics, Impactanalytics, Reward analytics, Behavior analytics, Recruitment analytics,Geo analytics, Effectiveness analytics and Prioritization analytics. TheApplication Logic layer 410 interfaces with the analytics APIs and istailored for a specific type of application 414.

A user interface, Web Applications, and Apps (e.g., 414) interact withthe backend system 402 to enable citizens and city management to conducteffective citizen engagements. An example of ‘a type of application’ 414may include sustainability applications, e.g., for water andelectricity. Such sustainability applications provide residents andbusinesses with information (of a history of their consumption) andinsights and/or recommendations (of their usage patterns, trend,forecast of usage and/or ways and activities to reduce consumption)through technology and analytics and enable them to, e.g., conservewater and electricity consumption, save bills on utilities, and improvethe environment, and others. The behavior analytics can assess howinformation and technology affect the behavior of the users, e.g.,whether or not users actually changed their behavior to conserve waterand/or electricity while using the sustainability application, and ifyes, and what was the actual savings.

FIG. 5 is a diagram illustrating an example data flow in one embodimentof the present disclosure. The Adapter layer 502 comprising APIs or thelike interfaces to data sources may receive data from data sources. Forexample, a social computing adapter 504 or API or the like may receivesocial profile data from social computing channels 506. Communicationsadapter 508 or API or the like may receive from and send tocommunication channels 510, event data. HRA adapter 512 or the like mayreceive survey data, goals, and recommendations from HRA sites 514. Oneor more other data adapters (e.g., 516) or the like may interface toother data sources 518 for receiving and/or sending data to thosesources.

The Factory layer 520 integrates the data obtained via the adapter layer502 into the data model 522. For example, various data from the variousdata sources are extracted and formatted into the appropriate format andstored as attributes (values) of the data model. That is, the Factorylayer 520 provides data management, integration, process orchestrationand integrates with the rest of the components, e.g., data model, socialcomputing adapters and/or APIs, communication adapters and/or APIs,monitoring and tracking portal for computation and display of the keyperformance indicators (KPIs), and backend processing for multiplesensor-based smartphone apps.

The Engagement Analytics layer 524 uses the data stored according to thestructure of the data model to create appropriate analytics, e.g.,Trust, Impact, Reward, Behavior, Recruitment, Geo, Effectiveness, andPrioritization.

The Application Logic layer 526 processes the results of the analyticsperformed in the Engagement layer 524, and provides the data to users,e.g., via user interface programs (e.g., application portals, mobileapplications, etc.) 528. Application Logic layer 526 is furtherdescribed with reference to the Application Logic at 310 in FIG. 3.

Thus, in one embodiment, a method for providing end-to-end citizenengagements, may comprise obtaining data, using a computer processor,from one or more of communication and social computing channels via oneor more adapters. The obtaining of the data further may includereceiving data from one or more sensing devices that sense one or moreactivities of one or more participants of the end-to-end citizenengagements. The obtaining of the data may further include communicatingwith a remote application installed on a mobile device to receive thedata. The obtaining of the data may be performed by one or more adaptersand application programming interfaces executing on the processor forcommunicating with at least the one or more of communication and socialcomputing channels.

The method may also include providing data refactoring and management,integration and process orchestration, using the computer processor, ofthe data according to a data model as data attributes of the data model,the data obtained from multiple disintegrated data sources. The datamodel may specify a set of data types, which may include at leastRecord, Verification, Impact, Location, Actor, RegisteredActor, Comment,Attachment, Account, Preference, Subscription, Notification, Incentive,ActorAction, Template, Relationship, Team, Teammember, and Teamgoal datatypes.

The providing of data management and integration may further includeperforming adaptation, extraction, inference, reduction andaugmentation, and integration of the multiple disintegrated datasources.

The integrating and process orchestration may further compriseintegrating with a monitoring and tracking portal, e.g., for computationand display of the statistics, e.g., key performance indicators (KPIs)computation and display, integration with one or more social computingadapters, integration with one or more communication adapters,integration with other adapters, e.g., for Health Risk Assessment (HRA)sites, which is a third party system that provides auxiliaryinformation, e.g., a health risk assessment survey, as well asintegration with backend processing for multiple sensor-based smartphoneapps.

The one or more analytics may be performed by invoking one or moreapplication programming interfaces corresponding to the one or moreanalytics, the one or more application programming interfacescorresponding to one or more of Trust analytics, Impact analytics,Reward analytics, Behavior analytics, Recruitment analytics, Geoanalytics, Effectiveness analytics and Prioritization analytics.

The method may also include performing, using the computer processor,one or more analytics based on the data attributes stored according tothe data model and input specified to the one or more analytics.

The method may also include providing, using the computer processor, oneor more results computed by performing the one or more analytics.

The method may further include producing, using the computer processor,one or more application logics supporting one or more front-endapplications. One application logic may be custom-tailored for oneapplication, e.g., desk-top application, Web-based applications and/orportals, smartphone applications, and the like.

The method may also include providing the one or more front-endapplications for automated sensing of user activities and sensor-basedpersonal assistant capability. The front-end applications may includeone or more of Smartphone apps for automated sensing of user microactivities. Sensor-based Personal Assistant Capability may be providedemploying sensing devices that function as a Per Personal Assistant forpersonalized the sensing. The Smartphone apps further may include one ormore functions, e.g., automated sensing, classification, self-correctionand learning of human micro mobility activities to produce accurateclassification of the type and duration of an activity, monitoring,tracking, goal-setting, teaming, providing alerts based on user'sactivity history, and optionally integrating with the HRA and providingalerts based on data collected to support HRA improvement, etc.

FIG. 6 is a diagram illustrating a use case for wellness program usingEECEASPA of the present disclosure in one embodiment, e.g., wellnessmonitoring and personalized incentive and tracking. Citizen EngagementPlatform (CEP) 602 of the present disclosure may receive data about userin various ways. CEP 602 comprises the back-end system architecturecomponents and functionality shown in FIG. 4 in one embodiment of thepresent disclosure. For example, survey input may be received fromparticipants 604 via a web portal or smart phone application 606 or thelike. The Citizen Engagement Platform 602 may receive data from the webportal 606 and also may provide alerts, insights, comparison metrics forparticipants via the web portal 606. The Citizen Engagement Platform 602also may receive data associated with participant activity sensing viamotion sensing smartphone apps 608. Optionally, the Citizen EngagementPlatform 602 also may also receive survey data from participants andwellness scores and recommendations for participants by interacting withcertain (Health Risk Assessment) HRA sites, which processes the surveydata from participants and provide wellness score and recommendations inreturn as shown at 610. If the wellness score and recommendations datawere received by the Citizen Engagement Platform 602, it displays suchdata to users of the wellness portal/smartphone application. The CitizenEngagement Platform 602 may provide wellness campaign management andmonitoring updates to wellness monitoring portal for organizationalentities 612.

FIG. 7 shows a sample flow that illustrates a use case for wellnessprograms using EECEASPA with HRA in one embodiment of the presentdisclosure. This embodiment may utilize a third party HRA site 710. At702, City recruits volunteers and launches CEP wellness program. At 704,volunteers log in and/or install smartphone application on theirdevices. They input profile information, for example, data associatedwith the volunteers (participants), which the CEP receives. At 706, CEPdelivers HRA survey to the volunteers. At 708, enrolled citizens (alsoreferred to as volunteers or participants) respond to HRA survey, whichCEP receives. At 710, CEP exchanges the information about the surveywith a third party to get HRA based metrics, e.g., health related scoresand recommendations, which may be determined by each HRA site. Any suchmetrics relevant to the scenario at hand for health and/or wellness canbe input to the Citizen Engagement Platform 602, which can in turndisplay the metrics to users. For example, if the program is fordiabetes prevention, the health metrics would be related to diabetes.Similarly, if the program is for healthy weight or heart diseaseprevention, the metrics may be related to diet, weight and/or heartdisease. At 712, volunteer mobility is sensed (e.g., by smartphone appthat senses activities) and reported into CEP. For instance, CEPreceives such data via its adapters. At 714, CEP analyzes mobility, HRAand all other inputs associated with each volunteer. For instance, CEP'sengagement layer may perform the analysis. At 716, CEP sharesinformation, insights and recommends actions and interventions withvolunteers. FIG. 13 shows examples of the shared information andinsights. At 718, volunteers engage in recommended activities and partof this is sensed and part of this is reported to CEP. At 720, CEPaggregates results and evaluates program effectiveness. At 722,City/program stakeholders understand engagement effectiveness. At 724,CEP thanks and rewards volunteers with incentive points, if any. In theexample flow shown in FIG. 7, an HRA site may provide score andrecommendations based on users' surveys. CEP may interact with the HRAsite to retrieve those score and recommendations and display them tousers.

FIG. 8 shows an example flow that illustrates another use case forwellness programs using EECEASPA in one embodiment of the presentdisclosure. At 802, City recruits volunteers and launches CEP wellnessprogram. At 804, Volunteers log in and/or install smartphone applicationon their devices. At 806, volunteer mobility is sensed (e.g., bysmartphone app that senses activities) and reported into CEP Application(App), e.g., a personal assistant app or the like. Volunteers canreview, log, edit, add, and otherwise update their physical activityentries, diet and weight entries in the CEP App. At 808, volunteers setgoals to track their own progress, e.g., monitoring and tracking ofphysical activities, diet, weight, and/or others, and receivingreminders from CEP App (e.g., the personal assistant app). At 810, agame that enables volunteer participants to compete by Team allowsteaming support for volunteers to compare their own progress withaggregate data of anonymous others. Briefly, the teaming support enablesvolunteers to form a team with a certain goal, e.g., healthy weight,join team, and compete by team. At 812, CEP aggregates results andevaluates program effectiveness. CEP provides reports to the City ofengagement progress. At 814, City/Program stakeholders understandengagement effectiveness. At 816, CEP thanks and rewards volunteers withincentive points, if any.

FIG. 9 and FIG. 10 illustrate a data model (e.g., shown in FIG. 3 at304) in one embodiment of the present disclosure. The data model in oneembodiment of the present disclosure may provide supports for variousdata (not an inclusive list): e.g. Record, Verification, Impact,Location, Actor, RegisteredActor, Comment, Attachment, Account,Preference, Subscription, Notification, Incentive, ActorAction,Template, Relationship, Team, Teammember, and Teamgoal, etc.

For example, the data model supports both registered users and anonymoususers. Data types associated with this type of support may include:Actor (anonymous users), RegisteredActor (users or organizations knownto Citizen Engagement Platform, and Account. Once a user/organizationbecomes a RegisteredActor that is known to the system, the user ororganization can have Incentive record. Both registered users andanonymous users can receive notifications. Any Actor (anonymous orregistered) can have Notification, Record (and related Incident,Announcement, i.e., proposal and investment), and ActorAction. The modelmay also support organizations and single users.

In addition, the data model may support at least three types based onone base data type (Record): Incident (i.e., Service Request),Announcement (for a proposal of a new initiative, e.g., clean air orsidewalk-pick on bulky furniture, etc., or an investment of a project,e.g., new building, highway, bus route, etc.). Record is anything thatis reported by the citizen and can be located on a map. It containscommonly used data fields including various fields for impact,verification, location and revision. Data type associated with this typeof support may include: Record, Incident, Announcement. Record hasseveral related data types: CategoryItem, Impact, Verification,Comments, Revision, MergeRecord, Area (for location and neighborhood).Record also includes various data, e.g., urgency, severity, certainty.Announcement has several related data types: Contact, Attachment (canbe: Results or Collection (e.g. files, images, videos, etc.)).

The data model of the present disclosure in one embodiment may alsosupport the following data types: Notification, Impact, Verification,Area, ActorAction, Incentive, NotifyTemplate. Notification may involveseveral other data types: Preference, Subscription (start and stoptimes), Location, Route (for location-based notification). Verificationmay involve several other data types: VerifyRule. Both Impact andVerification may be used together to arrive at one final determinationof the decision of an Incident in terms of the reliability of thesource, the user's reputation (e.g., trust score computed by trustanalytics), and the impact on users (via impact score computed by impactanalytics that analyzes the impact on the city residents of certainreported incidents, e.g., potholes, power outage, chemical gas leak,etc.,), etc. ActorAction is a general way to track all activities doneby an actor, e.g., ‘report’ an incident, ‘vote’ a proposal, ‘comment’ onanother actor's comment, ‘view’ portal page, etc. This can be used foranalytics, e.g., more ‘report’ activities than ‘viewing’ activities.NotifyTemplate is an instance of the notification at a point in time forease of reuse and customization. This also includes Preference,Subscription (start and stop times), Location, Route, etc. Users canreuse or change the templates without having to go through the troublecreating from scratch the entire list of data fields as described in thetemplate. For example, organizations may want to create templates forits employees. Also, one or more frequently used templates, e.g.,employee record, which may include commonly used list of data fields,e.g., employee ID, name, address, phone numbers, job title, date hired,emergency contact info, etc.), payroll record, customer profile, userprofile (e.g. social network sites), etc. can be reused easily or beensold by small business that specializes in custom-tailor notificationand alerting packages.

FIG. 10 describes a data model comprising data types that support Teamrelated functions, e.g., Team, Teammember, Teamgoal, and related themetrics for the team member and team goal. These data types enable usersto participate in teams and compete by team. A user can create a teamwith a goal, join a team, and leave a team. Each team has a total score,which is a tally of the scores of all its members. There may be rulesthat govern the winning team, e.g., the team that has the highest scorewins.

As described above, the data model is used to store the data receivedfrom various data sources and data used in the EECEASPA of the presentdisclosure, in a structured format for use by various functionalities ofthe EECEASPA.

FIG. 11 shows an example data flow via the factory layer (e.g., shown inFIG. 3 at 308) for performing trust and impact analytics in oneembodiment of the present disclosure. Social computing adapter 1102receives data from one or more social computing channels 1104, which mayhave social data 1106 stored associated with the one or more socialcomputing channels 1104. Sample data may include user ID (UID),Experience, Job responsible, Blog record, Wiki record, Social network.Communications adapter 1108 receives from one or more communicationchannels 1110 data associated with tasks 1112 which the communicationchannel 1110 may store. Sample data may include Task ID, Task type (takepic, input text, recruit volunteers, voting), Goal (#submissions),Coverage (spatial: zip code, polygon; temporal: from, to), Submissions(UID, time, quality, geo-tag). The Factory layer takes the data receivedby the Adapter layer, transforms the data into appropriate datastructure of the data model. Trust Analytics 1118 of the EngagementAnalytics layer takes UID and Job type data 1120 as input, and performscomputation. The resulting Trust value 1122 is provided to a user (e.g.,via the application logic and front end application). Impact Analytics1124 of the Engagement Analytics layer takes Task type and Coverage data1126 as input, and performs computation to produce output 1128 thatincludes the number of people, goal and progress.

FIG. 12 illustrates software module interaction and APIs in EECEASPAfactory layer in one embodiment of the present disclosure. An adapterlayer (e.g., social computing adapter) 1202 may use a RepresentationalState Transfer (REST) call to communicate with a data source (e.g.,social media channel) 1204. The data source may communicate anExtensible Markup Language (XML) or JavaScript Object Notation (JSON)data to the adapter 1202. The communication between the adapter 1202 andthe data source 1204 may be in asynchronous mode. The factory layer 1206may use a Java™ call to communicate with the adapter 1202. The adapter1202 may return Java™ objects to the factory layer 1206.

The Factory layer 1206 may call the following APIs to communicate withthe social computing adapter 1202 of the Adapter layer:

-   ProfileObject GetBasicProfile(integer UniqueID)

Returns a profile object identified by the UniqueID of a participant

-   ProfileObject[] GetProfiles(String Attribute)

Returns a list of profiles for those who identified specificskills/experience/other attributes in their profiles

-   ProfileObject[] GetProfiles(GIS LocationCriteria)

Returns a list of profiles for those who meet static location criteria

GIS refers to Geographic Information Systems.

The engagement analytics layer (e.g., shown in FIG. 3 at 312) mayperform the following analytics in one embodiment of the presentdisclosure.

Event Impact Analytics, based on pre-defined rules, links reportedincidents and comments to impacted parties, actions, and possibleoutcomes. As a use case example of Event Impact Analytics, when aserious pothole report is submitted, the event impact analytics analyzesthe impact of the reported event on the population of the city.

Incentive Design Analytics may generate various incentives such aspoints, badges, ranks, and so forth that are linked to reporting,polling, and other activities through Citizen Engagement Platform andevaluate the incentives that seem to be most effective. As a use caseexample of Incentive Design Analytics, higher points may be given tousers with fast and valuable reports to have high impact. As another usecase example of Incentive Design Analytics, give higher points to userswith valuable comments that help shape the public hearing process.

Participant Behavior Analytics analyzes how participants in engagementsbehave under various conditions. As a use case example of ParticipantBehavior Analytics, the analytics tries to understand classes ofparticipants that will communicate a pothole. Some users may reportevery pot hole they see. Others may report only if it has impacted themin a negative way, yet others may report when they see reward points fortheir prior reporting, etc.

Participant Reputation Analytics analyzes the quality of participantinput and links it to participant reputation to create varying degreesof trust in crowd-sensed data. As a use case example of ParticipantReputation Analytics, some citizens may report even the smallestproblems while others will only report if it is a big issue. Learningand associating a trust value to every participant input and using it toweigh and aggregate the overall degree of input from citizenparticipants help prioritize actions.

Engagement Effectiveness Analytics analyzes the amount of engagementbeing generated by various engagement mechanisms to evaluate theeffectiveness of those mechanisms. As a use case example of EngagementEffectiveness Analytics, if recruitment is being conducted for an eventthrough multiple marketing channels, this analytics may evaluate theeffectiveness of each channel in terms of the success of recruitment.

Reaction Prioritization prioritizes possible choices of reactions basedon participant reputation analysis and event impact analysis. As a usecase example of Reaction Prioritization, if there are two potholes tofix and only one can be fixed next week, the reaction prioritization maybe used to recommend the pothole that if fixed will lead to the bestmitigation of impact of the pothole and the mitigation of the negativeopinion generated by it.

Impact analytics may be a variation of the trust analytics.

The following illustrates analytics APIs in one embodiment of thepresent disclosure.

Task Trust Analytics

-   Input: Social Profile Object, Activity Data Object-   Argument: integer User ID (s), integer Activity Group ID-   Output: array of User ID (s), integer Activity Group ID, integer    Trust Metric

Task Impact Analytics

-   Input: Social Profile Object, Activity Data Object-   Argument: array of User ID (s), integer Activity Group ID-   Output: array of User ID (s), integer Activity Group ID, GIS    Location Coverage (or Time coverage)

Social Analytics

-   Input: Social Profile Object, Activity Data Object-   Argument: array of User ID (s), integer Activity Group ID-   Output: array of User ID (s), integer Activity Group ID, and Social    Weighted Network Graph

Each API may be implemented as an atomic calculation routine forselecting and prioritizing participants for a specific campaign task.For example, a social analytics API is used to identify individuals whohave a greater influence so that a social message can propagate moreeffectively. Another example is a trust API that assigns a higher scorefor individuals who have higher trustworthiness for a specific input. Animpact API is used to calculate who will be impacted by a specific eventand a campaign so that any inputs from the impacted individuals can beaccounted properly.

Analytics APIs may be implemented as abstraction classes: e.g., impactabstraction class to implement Event Impact Analytics, trust abstractionclass, prioritization abstraction class to implement ReactionPrioritization, behavior abstraction class to implement ParticipantBehavior Analytics, engagement effectiveness abstraction class toimplement Engagement Effectiveness Analytics, incentive abstractionclass to implement Incentive Design Analytics, and similarityabstraction class. A similarity API may use a set of clusteringalgorithms such as support vector machine, k-means, and others tocluster individuals into multiple clusters. A choice of distance metricssuch as L-1 norm, Cosine distance and other well known metrics may beused to calculate the similarity between clusters.

A trust API calculates a trustworthiness score of individual based on aset of attributes that describe a characteristics of individual andprior knowledge of a subject matter. In addition, previously verifiedinputs may be used to assign a score towards the trustworthiness.

The following illustrates an example use case scenario of transitapproval public hearing.

Trust Analytics calculate trust metric for a user by attribute types(e.g., responsiveness, quality of data, frequency of participation, andso forth). The analytics may be performed under the assumption that asEECEASPA runs, historical participation (and event) data is availablefor calculation. Three phases in which the analytics may be used are asfollows:

Recruitment Phase:

When recruiting volunteers, pre-calculated trust metric values are usedto prioritize a set of candidates that will contribute better than thosewho have smaller trust values. For example, in a transit approvalscenario, one would like make sure that participation and response rateis high. In this case, highly responsive people (thus hold high trustmetric value) may be chosen.

Progress Phase:

This phase reports on participation rate from trust-worthy volunteersand creates a list of future volunteers.

Evaluation Phase:

When analyzing data once a campaign is completed, trust metrics are usedto give different weight (to improve the reliability of the response).For example, in a transit approval scenario, one would want to puthigher weight to those who actually have ridership.

Impact Analytics calculates impact metric for a user by attribute types(e.g., geographical coverage (polygon, list of zip code, etc.),time-of-day event distribution). This analytics may assume that EECEASPAruns, historical participation (and event) data is available forcalculation. Three phases in which the analytics are used may be asfollows:

Recruitment Phase:

A campaign has coverage requirement. This pre-calculated coverage metricis used to cover the area of interest when recruiting people. Forexample, in a transit approval scenario, one would want to make surethat opinions are collected from those who are affected by the transitroute changes. This analytics allows finding candidates who need to berecruited.

Progress Phase:

This phase provides for run-time coverage impact graph, and helpsmonitor campaign progress in terms of coverage map and identify where acampaign owner should recruit more people (and encourage moreparticipation).

Evaluation Phase:

When analyzing data once a campaign is completed, impact metrics areused to normalize responses from different geographical areas atdifferent time. For example, in a transit approval scenario, one wouldwant to put higher weight to those who actually have ridership withinthe changed routes at different time.

Social Analytics identifies social links by hierarchical structure,friendship, historical collaboration through campaign activities, and soforth. This analytics may assume that as EECEASPA runs, historicalparticipation (and event) data is available for calculation. Two phasesin which the analytics may be used are as follows:

Recruitment Phase:

When recruiting people for a campaign, it would be easier to propagatemessages through social links. The identified social links is used forimproved communication methods. In some cases, one may want to avoidover-participation from a group of people, for example, large number ofpeople from a local interest group to vote for their interest. Forexample, in a transit approval scenario, one would want to make surethat opinions come from a diverse group of people to reduce bias. As therecruiting is in progress, this social links are used to identify whichgroup of people should be recruited more.

Evaluation Phase:

When analyzing data once a campaign is completed, social links are usedto reduce bias due to social relationships. For example, in a transitapproval scenario, if many data points from employees of a bus companyare gathered, one would want to make sure that one gets as many datapoints from non-employees.

For social communication adapters, there may be following two datastructures, e.g., Profile (for users, anonymous and registered) andCommunity (for organizations/groups, social relationships related data),Profile, Community, and Communities may be defined according thedefinition of Profile, Community, and Communities in IBM LotusConnections™ (Connections portal) from International Business Machines(IBM®) of Armonk, N.Y.

Examples of the data fields of Profile and Community and the APIs to beinvoked by the analytics layers to query information it needs are shownin Table 2. These APIs definition are from IBM Lotus Connection and usedas-is. The description below is to show their usage in this disclosure.

TABLE 2 The Example definition and Usage Profile and Community APIsProfile definition: UID - unique id that identifies the participant role(anonymous, admin, city staff, mayor, volunteer, campaigner, etc. ...)zip code (home location) - zip code of the participant home GIS locationtitle first name last name address mobile - mobile phone number deviceId - smart phone device ID preferredContactMethod - e.g. email, phone,text, etc. Profile APIs: Profile getProfile(String uid); Profile may befor a user of the site who performs these activities on the site: e.g.,follow, submit or comment on events or whose comments are being followedor commented on, etc. Profiles may contain location information such aszip code and other location information, e.g., which can be extractedfrom the city database with the permission from city, or obtained frommobile phone (GIS info). Usage scenarios: 1. The location informationcan be used to determine coverage of how people signing up with acampaign. Community APIs: Community definition: Name - name of thecommunity contact UID - contact UID of the community phone number -phone number of the community The location information can be used tofilter data by location proximity, e.g., find my friends within 5 milesradio of where I live. Community getCommunity(String user, integermiles); Get community of this uid (user) within a certain miles. Usecase: Alex wants to start a campaign within 5 miles Location filteringcan be done in profile adapter. Community getCommunity(String uid); Getcommunity of this uid without specifying location proximity. Communityalso can contain information of following event, followed events,members of and relationship information including recent contact,activity log, relationship, in groups.

Social media communication adapter APIs (e.g., part of the adapter layershown in FIG. 4 at 406) for performing Impact and Trust Analytics (e.g.,part of the engagement analytics layer shown in FIG. 4 at 412) mayinclude the following APIs:

-   SocialNetworkObject GetSocialLinks (integer Unique ID, String    SocialType)

Returns a network of UID for a unique ID by social types

Types include hierarchical (in job setting), friendship, collaboration,and so forth

-   Result PushProfile(Unique ID, SocialProfileObject)

Allow to push profile information to connections profile database fromEECEASPA layer

This API is used to push a list of individuals of target.

FIG. 27 illustrates software module interaction and APIs in EECEASPAfactory layer in one embodiment of the present disclosure. An adapterlayer (e.g., communication channel adapter) 2702 may use a REST call tocommunicate with a data source (e.g., City or like data) 2704. The datasource may communicate an XML or JSON data to the adapter 2702. Thecommunication between the adapter 2702 and the data source 2704 may bein asynchronous mode. The factory layer 2706 may use a Java™ call tocommunicate with the adapter 2702. The adapter 2702 may return Java™objects to the factory layer 2706.

The following APIs may be called by the EECEASPA factory layer 2706retrieve data from the Adapter layer 2702:

-   Event[] GetAllEvents (integer Unique ID)

Returns all the events submitted by the user as identified by the UniqueID

-   Event[] GetEvents(integer Unique ID, String Task Type)

Returns vents that a user submitted by task type (e.g. campaign type)

-   Event[] GetEvents(integer Unique ID, integer Task ID)

Returns events of a user for a specific task ID (e.g. campaign ID)

As an example, the APIs and its inputs and outputs of the TrustAnalytics (for processing ‘Profiles’) of the engagement analytics layerare described as follows. Input to the Trust Analytics module thatcomputes trust metrics may include attribute names and trust calculationrules, which defines the weight of each attribute name based on thenature of the campaign. For example, for a transit campaign, a campaigndesigner would define three key attributes: (‘experience’, ‘assign 0.3if experienced in mass transit industry’); (‘jobResp’, ‘assign 0.5 ifresponsibility includes x, y, or z’ related to mass transportation);(‘location’, ‘assign 0.2 if location is within 10 miles from zip code1x201’). The Trust Analytics also receives as input, such data like XMLformatted data that is integrated into the data model (e.g., FIG. 5 at522), by a factory layer (e.g., FIG. 5 at 520) from social mediacommunication channel (e.g., FIG. 5 at 506) via an appropriate adapter(e.g., FIG. 5 at 504) in the adapter layer (e.g., FIG. 5 at 502). Thedata the factory layer (e.g., FIG. 5 at 520) process may includeinformation like “John was a former employee of local Department ofTransportation (DoT) responsible for improving ridership in the city,but does not live in the zip code 1x201.” The Trust Analytics modulethen may output, “John has initial trust level of 0.8.” where 0.8 is thetrust metric representing the trust level.

In another example, Trust Analytics (for processing ‘PriorActivities/Tasks’) API and its Inputs and Outputs are described asfollows. Inputs to the Trust Analytics module may include task type andtrust calculation rules, which define the weight of the task. Forexample, a campaign designer defines input as (‘Voting’, ‘Calculateaverage response rate’). Using data from data sources (e.g., City publicdata received via the adapter layer and integrated into the data modelby the factory layer) such as “John has participated in 10 votingcampaigns, and actually voted 8 times,” the Trust Analytics module mayoutput, “John's response rate for voting campaigns is 0.8.”

In one embodiment of the present disclosure, Trust API member functionsof the TrustAnalytics may include the following:

-   Trust(SocialProfile, Campaign)-   SocialProfile—a data structure, e.g., Java™ class, that describes    profile of a participant, which may include a Unique ID that    identifies the participant, job type, location, e.g., zip code,    name, address, etc.-   Campaign—a data structure, e.g., Java™ class, that describes a    campaign, which may include the goal and/or objective, duration,    total budget, etc.-   Public IndividualMetrics getMetrics( )—Return the set of metrics    computed for the participant.-   Public Boolean updateMetrics( )—Returns ‘true’ if metric are updated    successfully, ‘false’ otherwise.-   Public Boolean calculateMetrics( )—Returns ‘true’ if metric are    calculated successfully, ‘false’ otherwise.

FIG. 28 is a diagram showing an example of input and output for ImpactAnalytics (e.g., part of engagement analytics layer shown in FIG. 5 at524) in one embodiment of the present disclosure. The Impact Analyticsshown in FIG. 28 filters by criteria. Input 2802 to this analytics mayinclude an array list of unique IDs of participants (ArrayList<Long>uniqueIDs). Argument (another input 2802) may include an array list ofcriteria 2804. ‘Criteria’ is a data structure, e.g., Java™ class thatdescribes personal attributes and conditions (e.g., geographical range,demographic information, financial status). Output 2806 of thisanalytics may include an array list of impacted unique IDs(participants).

As another example, Impact Analytics (Profile) and its inputs andoutputs are described as follows. Inputs may include attribute name andimpact calculation rule. For example, a campaign designer defines threekey attributes: ‘location’, ‘extract location tags from social mediaactivities’. Impact Analytics uses data obtained from data sources viaone or more appropriate adapters of the adapter layer and integrated bythe factory layer to produce its output. Example data may include “Johnhad home location, and location tags throughout wiki/forum activities,indicating his coverage profile.” The Impact Analytics module mayproduce as output, “John's coverage impact is represented in an orderedlist of location traces.” Data searching, data mining or extraction,and/or text processing techniques may be used to produce such output.

Yet as another example, Impact Analytics (Prior Activities) and itsinputs and outputs are described as follows. Inputs may include tasktype, impact calculation rules). For example, a campaign designerdefines inputs as: (Ticture Taking', ‘Heat map of prior activities’),(Ticture Taking', ‘Time of day distribution of prior activities’).Impact Analytics uses data obtained from data sources via one or moreappropriate adapters of the adapter layer and integrated by the factorylayer to produce its output. Example data may include, “John has beenreporting various pictures through many prior campaigns.” The ImpactAnalytics module may produce as output, “John's historical impact isrepresented in the form of heat map of prior activities as well as timeof day distribution.”

In one embodiment of the present disclosure, Impact Analytics API memberfunctions may include:

-   Impact(SocialProfile, Campaign)-   SocialProfile—a data structure, e.g. Java™ class, that describes    profile of a participant, which may include a Unique ID that    identifies the participant, job type, location, e.g., zip code, etc.-   Campaign—a data structure, e.g., Java™ class that describes a    campaign, which may include the goal and/or objective, duration,    total budget, etc.-   Public ArrayList<String>getSociallmpact( )

Returns a list of social impacts.

-   Public Boolean geographicallylmpacted( )

Returns ‘true’ if the participant is geographically Impacted, ‘false’otherwise.

-   Public Boolean temporallylmpacted( )

Returns ‘true’ if the participant is temporally Impacted, ‘false’otherwise.

-   Public Boolean sociallylmpacted( )

Returns ‘true’ if the participant is socially impacted, ‘false’otherwise.

In one embodiment of the present disclosure, Recruitment API memberfunctions may include:

-   Recruitment(SocialProfile, Campaign)-   SocialProfile—a data structure, e.g., Java™ class, that describes    profile of a participant, which may include a Unique ID that    identifies the participant, job type, location, e.g., zip code, etc.-   Campaign—a data structure, e.g., Java™ class that describes a    campaign, which may include the goal/objective, duration, total    budget, etc.-   Public Boolean isTarget( )

Returns ‘true’ if the participant is among the targeted population forthe campaign, ‘false’ otherwise.

-   Public Boolean isRecruited( )

Returns ‘true’ if the participant is recruited for this campaign,‘false’ otherwise.

-   Public Boolean hasViewed( )

Returns ‘true’ if the participant has viewed this campaign, ‘false’otherwise.

-   Public Boolean needReminder( )

Returns ‘true’ if the campaign needs to send out a reminder toparticipants, ‘false’ otherwise.

-   Public String bestChannel( )

Returns the best channel for the campaign, e.g., the channel that hasrecruited the most participants.

-   Public Boolean needPublicityblast( )

Returns ‘true’ if the campaign needs a Publicity blast, ‘false’otherwise.

-   Public String returnEmail( )

Returns the participant's email address.

-   Public String profileID( )

Returns the participant's profilelD.

-   Public Integer viewCount( )

Returns the count of the number of viewers of the campaign.

CEP App (e.g., FIG. 3 at 316) provides functionalities for logging,editing, adding entries of physical activities (e.g., shown in FIG. 15,FIG. 16, FIG. 17, FIG. 18), diet and weight (e.g., shown in FIG. 19,FIG. 20, FIG. 21). For example, CEP App allows participants to entertheir own physical activities, e.g., number of minutes spent on running,busing, driving, and also the intensity of the physical activities,e.g., low, moderate, high. Using CEP App, participants may also enterinformation such as the quantity of certain foods consumed, e.g.,fruits/vegetable servings, snacks, rich desserts, etc.

CEP App also allows for goal setting, logging based on goals, andmonitoring and tracking of the progress of the goals (e.g., FIG. 19,FIG. 20, FIG. 21). For example, a participant may set personal goal andlog associated data based on the goals. For instance, users(participants) may log the type and quantity of certain foods consumed,e.g., snacks, fruits/vegetable servings, deserts, etc., and eatingfrequencies/intervals, weight, and physical activities.

The personal assistant App, CEP App, also enables team comparison and/orcomparison with anonymous aggregate data of other participants. Forexample, physical activities of a participant or a group of participants(team) may be compared with statistic data such as Average WalkingMinutes of all active users of the CEP App (e.g., per day, per week),Average Running Minutes of all active users (e.g., per day, per week),Average Driving Minutes of all active users (e.g., per day, per week).As another example, diet of a participant or a group of participants(team) may be compared with statistic data such as Average quantity ofcertain foods consumed, e.g., fruits/vegetable servings, snacks, richdesserts, etc., of all active users (e.g., per day, per week), Averagefrequency/interval of certain foods consumed of all active users (e.g.,per day, per week). Yet as another example, weight of a participant or agroup of participants (team) may be compared with statistic data such asAverage weight of all active users (e.g., most recent).

CEP App may be a mobile application downloaded and installed on a user'smobile device, e.g., smartphone. CEP App may provide authenticationfunctionalities to authenticate a user to use the CEP App. For example,CEP App may display a login screen for the user to enter a user ID andpassword in order to access and use the application. Also, a user may bepresented with an end user license agreement (e.g., on a display screenof the mobile device), which the user would agree to accept, e.g., byclicking a button on a display screen. In one aspect, the logininformation (e.g., user ID and password) may be provided by a city,e.g., the CEP platform owner.

FIGS. 13-26 shows example user interface screens of the CEP App, viawhich a participant may enter data, view data, and otherwise interactwith the CEP of the present disclosure in one embodiment. The CEP Appmay display summary screen from which a user may be able to navigate toa dashboard page (screen display), logging page (screen display),activities page (screen display), teams page (screen display), and apage (screen display) of more functionalities. Dashboard may show threepossible pages: Wellness page shows Summary Statistics; Smarter Waterpage can show smarter water meter data, provided that the household hasa smart meter for water; Smarter Electricity page can show smarterelectric meter provided that the household has a smart meter forelectricity data. FIG. 13 shows an example wellness dashboard that maybe displayed by the CEP App in one embodiment of the present disclosure.Wellness page may show summary statistics such as displaying high-levelstatistics of physical activities: e.g., number of minutes spent ondriving, running, walking. Wellness page may also show messages such ashistorical activity data, reminders. Wellness page may also show adisclaimer, for users to review.

In one embodiment of the present disclosure, smarter water and/orsmarter electricity data may be linked with CEP App. For example, if auser owns an anonymous unique identifier (UID) that was previously usedto participate in the Smarter Water Portal and/or the SmarterElectricity Portal (aka “Primary UID”), the user may be able to see hiswater data and/or electricity data associated with the user's smartmeters when using this UID to log in. Any other UIDs assigned by theCity can only see the person's own wellness data but can be added by a“Primary UID” and be authenticated to see the view of water and /orelectricity data for the household. For example, FIG. 14 shows anotherDashboard page, on which a UID that is not associated with any watersmart meter, cannot see any water.

Logging page enables logging, goal setting, monitoring/tracking based ongoals of physical activities, diet and weight. FIG. 19 shows an examplelogging page. A user can select ‘Setup Log’ from Log page to add entriesin ‘Log Setup’ page to enable tracking. FIG. 20 shows an example logsetup page. FIG. 21 shows a log setup entry page via which a user mayenter goals, and track progress.

Activities page displays users' physical activities history for users toreview and enables users to add new activities as well as edit/correctthe activities from the history. FIG. 15 illustrates a physicalactivities and history display page in one embodiment of the presentdisclosure. FIG. 16 illustrates a page through which entries may beadded. For instance, selecting (e.g., clicking) an ‘add’ button from theactivities page (FIG. 15) may display the ‘add activity’ page shown inFIG. 16. FIG. 17 is another example screen illustrating a physicalactivities page in one embodiment of the present disclosure. FIG. 18shows another example of a page via which entries may be edited. Forinstance, selecting (e.g., clicking) an activity (e.g., ‘walking’) fromthe activities screen show in FIG. 17 may navigate to the display shownin FIG. 18 for editing the ‘walking’ activity.

‘Teams’ page shows comparison with anonymous aggregate data of otherparticipants based on the goals. For example, users can start one ormore new team with some description of the team's objective, e.g.,healthy eating or weight loss, etc. and invite others to join the team.Functions for teams may include: create, join, leave, delete the team(function names could change but capabilities would be similar to what'sdescribed here). Via the ‘teams’ page, users may view comparisons oftheir own data with anonymous aggregate data of other participants. Forexample, user's physical activities can be compared with Average WalkingMinutes of all active users (e.g., per day, per week), Average RunningMinutes of all active users (per day, per week), Average Driving Minutesof all active users (e.g., per day, per week); user's diet informationmay be compared with Average quantity of certain foods consumed, e.g.,fruits/vegetable servings, snacks, rich desserts, etc., of all activeusers (e.g., per day, per week), Average frequency and/or interval ofcertain foods consumed of all active users (e.g., per day, per week);user's weight information may be compared with Average weight of allactive users (e.g., most recent).

‘More’ page may show additional functions, e.g., which may be used lessfrequently. For example, ‘more’ page may include the following menuitems: Application Status shows application, network, and deviceinformation; License Agreement shown for the first time use of the App;Send Feedback enables users to send comments via email; SensorIdentifier enables the App to retrieve user's activities based on thesensor ID; Sign Out logs a user off the App; Setup Log enables users toset up goals for monitoring and tracking. FIG. 22 shows an example‘more’ page in one embodiment of the present disclosure. FIG. 23 shows‘application status’ page navigated from the ‘more’ page in oneembodiment of the present disclosure. FIG. 24 shows a page navigatedfrom the ‘more’ page, through which feedback may be composed. FIG. 25shows an example ‘sensor’ page via which a user may enter a sensor ID.CEP App then may communicate with the identified sensor to automaticallyretrieve sensor data from the sensor device identified by entered sensorID. FIG. 26 shows an example of a log out page in one embodiment of thepresent disclosure, which a user may use to sign out of CEP App.

In another aspect of the present disclosure, method and apparatus forconducting effective surveys and improving the return of userparticipation rate through impact analysis and tuning may be provided. Amethod and apparatus for conducting effective surveys and improving thereturn of user participation rate through impact analysis and tuning mayimprove the return of user participation rate for an effective surveybased on impact analysis, analyze user's potential impact to the surveybased on a set of selected attributes such as financial (salary earned,household income, etc.), age, location, education, attitude, etc., for atargeted scenario or scenarios, and tune and adapt the survey to a setof targeted candidates based on the selected attributes to improve therate of user participation. Such method and apparatus may provide areminder mechanism that adjusts frequency and communication channels byanalyzing past survey response characteristics of similar participantswherein similar participants were the participants who have similardemographic attributes.

Yet in another aspect of the present disclosure, method and apparatusfor effectively analyzing the accuracy and trustworthiness of surveyanswers through reputation analytics may be provided. A method andapparatus for an effective way of analyzing the accuracy ortrustworthiness of the survey answers through reputation analytics mayidentify the participant's reputation based on prior participationactivities, e.g., prior responses to survey questions and citizenengagement programs, as well as social profiles, etc. Such method andapparatus may analyze and aggregate survey answers based on eachparticipant's level of trustworthiness via one or more criteria, e.g.,reliability, responsiveness, prior experience, and prior surveyactivities, etc . . . . Such method and apparatus may also quantify aparticipant's trustworthiness for a target survey based on the goals andcontexts of the survey. Such method and apparatus may also analyze thetrustworthiness of a participant via reputation analytics that combinesone or more attributes, e.g., a person's occupation, personalexperience, the composition of a person's prior survey participationhistory based on a tuple of (correctness, consistency, relevance), andeducation level, etc., and provide a context parsing and profilingmechanism that analyzes survey contents and corresponding attributes ofeach participant.

Yet in another aspect of the present disclosure, method and apparatusfor improving survey participation rate with an incentive mechanism maybe provided. A method and apparatus for improving survey participationrate with an incentive mechanism that optimizes the incentive returnsmay analyze and aggregate survey types and goals to identify targetcustomers and incentives. Such method and apparatus may analyze targetcustomers and incentives by using results of similar surveys conductedpreviously; Identify target customers based on demographic attributesfor those who with high tendency to respond to changes in incentiveamount, frequency, and latency; Calculate incentives based on changes inresponse rates at the time incentives are given; Provide an incentivedelivery mechanism where the incentive analytics divides the totalamount of incentives provided at the survey design time into smallerchunks where each smaller chunk is being offered to those who willlikely accept it by completing survey questions to increase thepotential response rate of the participants; and optimize the incentivereturns by identifying the ‘optimal’ amount of the incentive of thesmaller chunk so as to maximize the number of participants that canreceive the incentive, and deliver it in a variable amount for thesmaller chunk based on a participant's reputation and/ortrustworthiness, e.g., more incentive for more trustworthy participants.

Yet in another aspect, method and apparatus for automated and effectivesensing, detecting, and classifying human micro mobility activities toproduce accurate classification of the type and duration of an activitymay be provided. A method and apparatus for automated and effectivesensing, detecting, and classifying human micro mobility activities toproduce accurate classification of the type and duration of an activitymay sense the human daily micro mobility activities, e.g. jogging,jumping, stepping motion, e.g., treadmill, stepping up and down, e.g.,aerobic exercises, using sensing devices, e.g. mobile phone; Engageusers to actively give feedback in a diary and/or other logging and addnew activities that are not sensed by the system; Enable user-assistedlearning to allow users to edit and reclassify system logged activitiesto improve accuracy; Detect patterns of misclassification; andAutomatically correct misclassification through learning obtainediteratively through data provided by users, e.g., new and editedactivities.

FIG. 29 illustrates a schematic of an example computer or processingsystem that may implement a CEP system in one embodiment of the presentdisclosure. The computer system is only one example of a suitableprocessing system and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the methodologydescribed herein. The processing system shown may be operational withnumerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with the processing system shown in FIG. 29 may include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may implement CEP functionalities 10that perform the methods described herein. The module 10 may beprogrammed into the integrated circuits of the processor 12, or loadedfrom memory 16, storage device 18, or network 24 or combinationsthereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), a portable compact disc read-only memory (CD-ROM), an opticalstorage device, a magnetic storage device, or any suitable combinationof the foregoing. In the context of this document, a computer readablestorage medium may be any tangible medium that can contain, or store aprogram for use by or in connection with an instruction executionsystem, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages, a scripting language such as Perl, VBS or similarlanguages, and/or functional languages such as Lisp and ML andlogic-oriented languages such as Prolog. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider).

Aspects of the present invention are described with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The computer program product may comprise all the respective featuresenabling the implementation of the methodology described herein, andwhich—when loaded in a computer system—is able to carry out the methods.Computer program, software program, program, or software, in the presentcontext means any expression, in any language, code or notation, of aset of instructions intended to cause a system having an informationprocessing capability to perform a particular function either directlyor after either or both of the following: (a) conversion to anotherlanguage, code or notation; and/or (b) reproduction in a differentmaterial form.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

Various aspects of the present disclosure may be embodied as a program,software, or computer instructions embodied in a computer or machineusable or readable medium, which causes the computer or machine toperform the steps of the method when executed on the computer,processor, and/or machine. A program storage device readable by amachine, tangibly embodying a program of instructions executable by themachine to perform various functionalities and methods described in thepresent disclosure is also provided.

The system and method of the present disclosure may be implemented andrun on a general-purpose computer or special-purpose computer system.The terms “computer system” and “computer network” as may be used in thepresent application may include a variety of combinations of fixedand/or portable computer hardware, software, peripherals, and storagedevices. The computer system may include a plurality of individualcomponents that are networked or otherwise linked to performcollaboratively, or may include one or more stand-alone components. Thehardware and software components of the computer system of the presentapplication may include and may be included within fixed and portabledevices such as desktop, laptop, and/or server. A module may be acomponent of a device, software, program, or system that implements some“functionality”, which can be embodied as software, hardware, firmware,electronic circuitry, or etc.

The embodiments described above are illustrative examples and it shouldnot be construed that the present invention is limited to theseparticular embodiments. Thus, various changes and modifications may beeffected by one skilled in the art without departing from the spirit orscope of the invention as defined in the appended claims.

What is claimed is:
 1. A computer-implemented method, comprising:receiving data from sources, wherein at least some of the sources aredisintegrated sources; refactoring and integrating the data according tothe data model as data attributes of the data model; performing at leastone analytics based on at least some of the data attributes and inputspecified to the at least one analytics, the at least one analyticsperforming at least a trust analytics that determines a trust metric fora campaign participant; and transmitting at least one result computed byperforming the at least one analytics.
 2. The computer-implementedmethod of claim 1, wherein at least some of the data comprises sensordata.
 3. The computer-implemented method of claim 1, further comprisingexecuting an application programming interface to communicate with atleast one of the sources to receive at least some of the data.
 4. Thecomputer-implemented method of claim 1, further comprising providing anapplication for detecting at least some of the data.
 5. Thecomputer-implemented method of claim 1, wherein the at least one resultindicates at least an effectiveness of a program within a geographiclocation.
 6. The computer-implemented method of claim 5, wherein theeffectiveness is determined based on aggregated data aggregating atleast some of the data as a function of different weights associatedwith different campaign participants, the different weights assignedbased on performing the trust analytics.
 7. A computer program productcomprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya device to cause the device to: receive data from sources, wherein atleast some of the sources are disintegrated sources; refactor andintegrating the data according to the data model as data attributes ofthe data model; perform at least one analytics based on at least some ofthe data attributes and input specified to the at least one analytics,the at least one analytics performing at least a trust analytics thatdetermines a trust metric for a campaign participant; and transmit atleast one result computed by performing the at least one analytics. 8.The computer program product of claim 7, wherein at least some of thedata comprises sensor data.
 9. The computer program product of claim 7,wherein the device is further caused to execute an applicationprogramming interface to communicate with at least one of the sources toreceive at least some of the data.
 10. The computer program product ofclaim 7, wherein the device is further caused to provide an applicationfor detecting at least some of the data.
 11. The computer programproduct of claim 7, wherein the at least one result indicates at leastan effectiveness of a program within a geographic location.
 12. Thecomputer program product of claim 11, wherein the effectiveness isdetermined based on aggregated data aggregating at least some of thedata as a function of different weights associated with differentcampaign participants, the different weights assigned based onperforming the trust analytics.
 13. A system comprising: a hardwareprocessor; and a memory device couple with the hardware processor; thehardware processor operable to at least: receive data from sources,wherein at least some of the sources are disintegrated sources; refactorand integrating the data according to the data model as data attributesof the data model; perform at least one analytics based on the dataattributes and input specified to the at least one analytics, the atleast one analytics performing at least a trust analytics thatdetermines a trust metric for a campaign participant; and transmit atleast one result computed by performing the at least one analytics. 14.The system of claim 13, wherein at least some of the data comprisessensor data.
 15. The system of claim 13, wherein the hardware processoris further operable to execute an application programming interface tocommunicate with at least one of the sources to receive at least some ofthe data.
 16. The system of claim 13, further the hardware processor isfurther operable to provide an application for detecting at least someof the data.
 17. The system of claim 16, wherein the at least one resultindicates at least an effectiveness of a program within a geographiclocation.
 18. The system of claim 17, wherein the effectiveness isdetermined based at least on aggregated data aggregating at least someof the data as a function of different weights associated with differentcampaign participants, the different weights assigned based onperforming the trust analytics.